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Non-Small Cell Lung Cancer

SC Amivantamab Q4W Shows Promise in EGFR-Mutated NSCLC Treatment

by Dr. Michael Lee – Health Editor September 9, 2025

written by Dr. Michael Lee – Health Editor

Barcelona, Spain -‌ September 9, ⁢2025 – ⁢New data presented at the World Conference on Lung Cancer (WCLC) ‌suggest a subcutaneous (SC) formulation of amivantamab, administered every four weeks⁣ (Q4W) in combination with lazertinib, shows promising efficacy and tolerability as a⁣ first-line treatment⁤ for patients with EGFR-mutated advanced non-small cell ⁢lung cancer (NSCLC). Results from cohort 5 ‌of the PALOMA-2 trial indicate ‍high response rates and pharmacokinetic equivalence to intravenous ⁣(IV) dosing, possibly offering a more convenient treatment ⁤option.

the study, led by Scott SC, demonstrated⁢ the SC regimen yielded favorable tolerability, with a reduced incidence of administration-related reactions and low discontinuation rates.‍ Notably, 43% of the PALOMA-2 cohort 5 population included patients with brain metastases, and while ⁣detailed intracranial efficacy results are pending, the systemic response‌ rates are encouraging for this subgroup.Researchers acknowledge the need for longer follow-up to determine median duration of response ⁣(DOR), progression-free survival (PFS), and overall survival (OS), given the current median follow-up of‌ 6.5 months. Ongoing monitoring for thromboembolic ‌complications,despite prophylactic anticoagulation,is also‍ warranted. Future analyses will assess quality-of-life outcomes, ⁣patient-reported measures of treatment convenience,‍ and potential pharmacoeconomic‍ benefits.

“These findings‌ support the continued development of SC Q4W amivantamab as a convenient, effective frontline therapy for EGFR-mutated NSCLC,” Scott reported at WCLC.

The PALOMA-2 trial (NCT05498428) findings support ‌the viability⁣ of⁢ once-monthly SC amivantamab as a patient-centered ⁣option to IV administration, aligning with broader efforts ‌to optimize convenience and quality of life in the management of EGFR-driven NSCLC. Further research is ⁣being conducted, including the ‌MARIPOSA trial (NCT04487080), comparing⁣ amivantamab ‍and lazertinib combination therapy to osimertinib.

References:

Scott SC. First-line subcutaneous amivantamab plus chemotherapy in EGFR⁤ exon 20 insertion-mutated advanced NSCLC: results from PALOMA-2. Presented at: World Conference on ⁣Lung Cancer; Barcelona, Spain; ⁣September 6-9, 2025.
A study of amivantamab and lazertinib combination therapy ‍versus osimertinib⁣ in locally advanced ⁤or metastatic non-small cell⁢ lung cancer⁤ (MARIPOSA). Clinicaltrials.gov. Updated August 19, 2025. Accessed September⁤ 8, 2025.
A study of amivantamab in participants with advanced or metastatic solid tumors ⁤including⁣ epidermal growth factor receptor (EGFR)-MUTATED NON-SMALL CELL LUNG CANCER (PALOMA-2). Clinicaltrials.gov. Updated July‌ 18, 2025. Accessed September 8,⁤ 2025.

September 9, 2025 0 comments

Health

New method uses exosome stiffness for lung cancer gene detection

by Dr. Michael Lee – Health Editor July 30, 2025

written by Dr. Michael Lee – Health Editor

Lung Cancer Detected by ‘Stiffness’ of Tiny Particles

New technology analyzes exosome mechanics for early diagnosis

Scientists have pioneered a groundbreaking method to identify lung cancer by measuring the physical properties of minuscule particles shed by tumors. This innovative approach, focusing on exosome stiffness, promises a less invasive and more precise diagnostic tool.

Unlocking Exosome Secrets

Researchers at DGIST, under President **Kunwoo Lee**, have developed a technique utilizing atomic force microscopy (AFM). This method can distinguish lung cancer gene mutations by assessing the “stiffness” of exosomes, which are tiny vesicles released into the bloodstream from cancer cells.

This advancement facilitates swift and accurate analysis of individual exosomes. It is anticipated to pave the way for a novel liquid biopsy-based diagnostic method for lung cancer.

Tackling a Deadly Disease

Non-small cell lung cancer (NSCLC) represents the vast majority of lung cancer cases. Its often asymptomatic early stages lead to diagnosis at advanced, harder-to-treat phases, contributing to its high mortality rate. Conventional tissue biopsies can be taxing for patients and limit repeat testing.

The push for non-invasive liquid biopsy technologies, using blood samples, has gained significant traction. This new exosome stiffness analysis aligns with that objective.

Linking Stiffness to Genetic Mutations

The DGIST team, led by Senior Researchers **Yoonhee Lee** and **Gyogwon Koo**, isolated exosomes from NSCLC cell lines with specific genetic mutations. They analyzed nanoscale physical characteristics, including stiffness and height-to-radius ratios, using AFM.

Exosomes from cells with a KRAS mutation showed markedly increased stiffness. This suggests that changes in cell membrane lipids due to the mutation are mirrored in the exosomes. Exosomes from cells with EGFR mutations, including a resistant form, displayed similar physical properties, correlating with their shared genetic makeup.

AI Boosts Accuracy

To precisely categorize these nanomechanical signatures, the research team integrated artificial intelligence. Data on exosome height and stiffness was fed into a deep learning model (DenseNet-121) for classification.

The AI model successfully identified exosomes from A549 cells with 96% accuracy, achieving an overall AUC of 0.92. This highlights the potential for a next-generation liquid biopsy platform that relies solely on exosome physical properties, bypassing the need for fluorescent labeling.

Future Clinical Application

Senior Researchers **Yoonhee Lee** and **Gyogwon Koo** expressed optimism, stating, This study presents a new diagnostic potential to distinguish lung cancer with specific genetic mutations using only a small amount of exosome samples.

They added, We plan to actively pursue the practical application of this technology by integrating a high-speed AFM platform in clinical sample validation.

This innovation aligns with broader trends in cancer diagnostics, where advancements like Guardant360 CDx offer comprehensive genomic profiling from blood samples, detecting over 500 cancer-related genes with high sensitivity (Guardant Health).

July 30, 2025 0 comments

Health

Alzheimer’s protein found to drive lung cancer spread to the brain

by Dr. Michael Lee – Health Editor July 3, 2025

written by Dr. Michael Lee – Health Editor

Alzheimer’s Protein Linked to Lung Cancer Spread

Discovery may allow repurposing of existing drugs.

A newly published study reveals how a protein associated with Alzheimer’s disease facilitates the spread of lung cancer to the brain, potentially opening the door to using existing Alzheimer’s drugs to prevent cancer’s spread.

Key Development

The research, conducted by scientists at **McMaster University**, **Cleveland Clinic** and **Case Comprehensive Cancer Center**, focused on the protein BACE1’s role in brain metastases, where tumors spread to the brain from lung cancer. In non-small cell lung cancer, these tumors can occur in as many as 40% of patients.

According to **Sheila Singh**, director of **McMaster’s Centre for Discovery in Cancer Research**, “We’ve always associated BACE1 with Alzheimer’s disease, so to find it playing a major role in lung cancer brain metastases is an important discovery. It’s a reminder that cancer can hijack biological pathways in ways we don’t yet fully understand.”

CRISPR Activation Screen

A genome-wide in vivo CRISPR activation screen allowed the team to activate thousands of genes individually in lung cancer cells, which were then introduced into mice. Researchers observed that activating BACE1 significantly increased the likelihood of the cancer cells invading the brain.

Alzheimer’s Connection

The connection between BACE1 and Alzheimer’s stems from BACE1’s role in cutting the APP protein, which leads to the formation of brain plaques, a hallmark of Alzheimer’s disease.

Hope for Repurposed Therapies

With limited therapies currently available for cancer that has spread to the brain, the identification of BACE1 offers a new avenue. Drugs developed for Alzheimer’s, such as Verubecestat, which inhibits BACE1 activity, could potentially be repurposed.

Mice treated with Verubecestat experienced fewer and smaller tumors and had longer lifespans. Although Verubecestat showed initial promise in Alzheimer’s patients, it was discontinued during Phase 3 clinical trials in 2018 because positive benefit/risk was unlikely.

Lung cancer is the second most common cancer in both men and women, accounting for about 12% of all new cancers, with around 238,340 new cases estimated in the U.S. in 2023 alone (American Cancer Society).

Interdisciplinary Collaboration

“This study highlights how interdisciplinary partnerships can lead to breakthroughs in understanding and treating devastating diseases like brain metastases,” said **Shideng Bao**, a researcher in **Cleveland Clinic’s Department of Cancer Biology**. “By identifying BACE1 as a key player in the spread of lung cancer to the brain, we’ve uncovered a promising new avenue for therapeutic intervention that could ultimately improve outcomes for patients.”

The study was a collaboration between the **Sheila Singh Lab**, **Cleveland Clinic**, and **Case Comprehensive Cancer Center**.

Future Research

Further research is necessary to fully determine how effective this therapy may be in preventing lung cancer from spreading to the brain.

July 3, 2025 0 comments

Business

Machine Learning Tool Predicts Cancer Treatment Response Based on Genetic Mutations

by Chief editor of world-today-news.com February 12, 2025

written by Chief editor of world-today-news.com

Certainly!⁤ Here is the content you requested:

Key‌ findings from the study include:

KRAS mutations in advanced non-small⁢ cell lung cancer were linked to poorer response to a common treatment (EGFR inhibitors), suggesting alternative treatments may be needed.
NF1‍ mutations improved responses to immunotherapy‍ and worsened ⁢responses to certain targeted ⁣therapies,highlighting their complex role‍ in treatment.
PI3K ⁣pathway mutations, which regulate cell growth, had varying effects depending on cancer type, with diffrent responses in breast, melanoma, and renal cancers.
DNA repair pathway mutations improved ‌immunotherapy effectiveness in lung‌ cancer by increasing tumor instability.
Mutations in immune-related pathways were associated with better survival rates for⁣ lung cancer patients treated with immunotherapy, suggesting not all mutations hinder ‍treatment success.

A powerful predictive tool

While cancer treatments have traditionally followed a one-size-fits-all approach,where patients with the same type of cancer receive the same standard therapies,the study underscores the importance of precision medicine,which tailors treatment based on a patient’s⁤ unique genetic makeup.

Yet ⁣while vast amounts of mutation data exist

Revolutionizing Cancer‌ Treatment: AI and Genomics Join Forces for ‍Personalized Medicine

In a groundbreaking study published⁢ in Nature Communications, researchers from the University of Southern California (USC) have unveiled a ‌novel approach to cancer treatment ⁤that leverages artificial intelligence (AI) and genomics to personalize care for patients. The ‍study, lead by Dr.⁣ R. Liu,demonstrates how integrating⁣ vast amounts of clinical and genomic data with advanced statistical and machine‌ learning techniques ‍can reveal previously unrecognized mutation-treatment interactions.”Our goal was to find patterns that might not be obvious at first ⁤glance, ⁢and then translate these insights into real-world tools that can expand access to immunotherapy for people with cancer,” explained Dr. Liu. “One ‍key innovation lies in integrating huge‍ amounts of data with ⁢advanced statistical and machine learning techniques to ‍uncover previously unrecognized mutation-treatment interactions.”

The research team⁤ analyzed data from 78,287 patients with 20⁢ different types of cancer. By applying sophisticated computational‍ methods, they were ‍able to identify specific genetic mutations that respond better to certain treatments.This discovery could considerably enhance the precision ‌and‌ effectiveness of cancer treatment, tailoring therapies to individual patients’ genetic profiles.

While ‍further clinical trials are necessary to validate these findings, Dr. Liu sees this study as a crucial step toward making cancer treatment more precise and personalized. “This research shows the ⁣power of⁤ computational science in⁢ transforming complex clinical and genomic data into⁣ actionable insights,” she said.‍ “It’s deeply‍ fulfilling to contribute to tools and knowledge that can directly improve patient care.”

Key Insights from⁢ the Study

| Insight ‍ | Description ⁣ ⁢ ‍ ‍ ‍ ⁤ ⁢ ‍ |
|———————————————–|—————————————————————————–|
| Data Integration ⁢ ‌ | Integration of clinical and genomic data using AI and machine learning. ‍ |
| Mutation-Treatment Interactions ‌| Identification⁤ of specific genetic ‌mutations that respond to certain ⁣treatments.|
| Personalized Medicine ‌ ‍ | Potential to tailor cancer treatments to individual patients’ genetic profiles.|
| Clinical Trials ⁤ ⁤ | Further trials needed to ⁢validate findings and bring them to clinical practise.|

The Future of Cancer Treatment

The intersection of AI and genomics is poised to revolutionize the field of oncology. By providing a more ‌nuanced understanding of‌ how different genetic mutations respond to treatments, this research could lead to better outcomes for patients. As Dr. Liu noted, “It’s deeply fulfilling to contribute to⁤ tools and knowledge that can directly‌ improve patient care.”

For more information on this groundbreaking⁣ study, visit the University of Southern California and read the full paper in Nature Communications here.

Stay tuned for more updates ⁤on how AI and genomics are transforming the future‌ of medicine. Your feedback and questions are welcome in the comments section below.

February 12, 2025 0 comments